The role of extra-coronary vascular conditions that affect coronary fractional flow reserve estimation.
Bibliographic record
Abstract
The treatment of coronary stenosis is often based upon invasive high risk surgical assessment. The surgical assessment quantifies the fractional flow reserve (FFR), a ratio of distal to proximal pressures in respect of the stenosis. Non-invasive imaging-computational methodologies call for robust and calibrated mathematical descriptions of the coronary vasculature that can be personalized. In addition, it is important to understand non-vascular factors that FFR. In this preliminary work, a 0D coronary vasculature model capable of personalization was implemented. The model was used to demonstrate the roles of focal and extended stenosis (intra-vascular), as well as microvascular disease and atrial fibrillation (extra-vascular) on FFR. It was found that FFR the right coronary artery is maximally affected by disease conditions. Interestingly, the severity of both microvascular disease and atrial fibrillation were found to be secondary to their mere presence regarding the modelling based FFR estimation. The 0D model provides a computationally inexpensive instrument for in silico coronary blood flow investigation as well as clinical-imaging decision making. Further- more, it establishes a basis for 3D computational fluid dynamics assessment of FFR in patient specific geometries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".